Package Pypi

HSTransform.egg-info/PKG-INFO

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+Metadata-Version: 2.1
+Name: HSTransform
+Version: 0.1
+Summary: A Package to Compute S-transform with Hyperbolic Window
+Author: Linh V Nguyen
+Author-email: [email protected]
+License: MIT
+Classifier: Development Status :: 4 - Beta
+Classifier: License :: OSI Approved :: MIT License
+Classifier: Programming Language :: Python :: 3.10
+Requires-Python: >=3.10

HSTransform.egg-info/SOURCES.txt

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+README.md
+setup.py
+HSTransform.egg-info/PKG-INFO
+HSTransform.egg-info/SOURCES.txt
+HSTransform.egg-info/dependency_links.txt
+HSTransform.egg-info/requires.txt
+HSTransform.egg-info/top_level.txt
+tests/__init__.py
+tests/test_hstransform.py

HSTransform.egg-info/dependency_links.txt

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+

HSTransform.egg-info/requires.txt

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+numpy
+scipy
+pandas
+matplotlib
+pytest

HSTransform.egg-info/top_level.txt

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+tests

README.md

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 ## 7. Citation
 
 If you use HS Transform in your research, please cite it as follows:
+Linh V Nguyen (2024). HS Transform (Version 0.1) [Computer software]. GitHub: github.com/nvlinhvn/hstransform

build/lib/tests/test_hstransform.py

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+"""
+Unit Test for HS functions
+- Test Gaussian Windowed
+- Test HS-transform output
+"""
+import numpy as np
+import pytest
+from HSTransform.hstransform import HSTransform
+
+def test_hyperbolic_gaussian():
+    """
+    Tests aims to validate the output 
+    of Gaussian Window of a time array
+    """
+    # Create a sinusoidal signal
+    t = np.linspace(0, 1, 100)
+
+    # Compute the S-transform
+    st = HSTransform()
+
+    # Test the _compute_hyperbolic_gaussian method
+    result = st._compute_hyperbolic_gaussian(1000, 50, t)
+    assert isinstance(result, np.float64)
+
+def test_fit_transform():
+    """
+    Tests aims to validate the S-transform output 
+    - shape
+    - type 
+    - check any null
+    """
+    # Create a sinusoidal signal
+    t = np.linspace(0, 2, 100)
+    signal = np.sin(2 * np.pi * 50 * t)
+
+    # Compute the S-transform
+    st = HSTransform()
+    s_transformed = st.fit_transform(t, signal)
+
+    # Add some assertions here to test the S-transform output
+    # Dimension
+    assert s_transformed.shape == (min(900, len(signal) // 2)+1, len(signal))
+    # Type of output
+    assert isinstance(s_transformed, np.ndarray)
+    assert np.issubdtype(s_transformed.dtype, np.complexfloating)
+    # any null
+    assert not np.isnan(s_transformed).any()
+
+if __name__ == "__main__":
+    pytest.main()